The present invention relates to natural language processing. More specifically, the invention relates to recognizing and resolving an analogical pattern in a multilingual scenario.
In the field of artificially intelligent computer systems, natural language systems (such as the IBM Watson™ artificially intelligent computer system or and other natural language question answering systems) process natural language based on knowledge acquired by the system. To process natural language, the system may be trained with data derived from a data source or corpus of knowledge, but the resulting outcome can be incorrect or inaccurate for a variety of reasons relating to the peculiarities of language constructs and human reasoning.
For example, analogies are language constructs which enable people to transfer knowledge from one situation or context (the source) to another (the target) based on a conceptual similarity there between, and provide powerful cognitive mechanisms or tools that can be used to explain something that is unknown in terms of a related concept that is known to someone. At the core of analogical reasoning lies the concept of similarity, but the process of understanding an analogy requires reasoning from a relational perspective that can be challenging Adding to the challenge is addressing the understanding and relationship across languages where word-for-word translation may not capture the essence of the original statement. In addition, automated systems and other natural language systems which come across an analogy in a question or answer corpus will also have a difficult time with identifying and understanding analogies. As a result, existing solutions for efficiently identifying and understanding analogies for training and/or use by a natural language processing system are extremely difficult at a practical level.